Decision Making Factors in Child Caregiver Reporting of Child Abuse and Neglect
Bibliographic record
Abstract
This study investigated decision making factors used by child caregivers to identify suspected child abuse and neglect and collected data on caregiver training in the recognition and reporting of suspected child abuse and neglect. Data was collected in July 1999 in fourteen north Texas childcare programs. One hundred twenty three teaching and administrative staff completed a survey based on Jacobson, A., Glass, J. and Ruggiere, P. (1998). Five teachers and five administrators chosen for convenience were read eleven vignettes describing possibly abusive situations to decide whether they were reportable or non-reportable, and to indicate factors used to make their decisions. Administrators (50%) and teachers (13.3%) reported being unfamiliar with child abuse and neglect definitions and reporting laws. Two thirds (66.7%) of the administrators and 39.8% of the teachers had received specific training in recognizing and reporting child abuse and neglect. Administrators were more likely than teachers to report suspected child abuse and neglect. Teachers often reported to program administrators rather than state designated authorities. All subjects relied on information about children, but administrators also used information about parents, with teachers more likely to make excuses for parental actions. With 110 reporting opportunities, training was cited as a factor only twice by administrators. No teachers made reports to anyone other than program administrators, a factor named deference in this study. Four of five administrators expected deference from teachers when reporting decisions were made. Present training in the recognition and reporting of suspected child abuse and neglect is inadequate. Caregivers need additional training in differences between accidental and intentional injuries, detection of child sexual abuse and emotional neglect, recognition and assessment of injuries among infants and toddlers, and mandated reporting procedures. Further research on optimal training for accurate reporting of suspected abuse and neglect is needed. A mandate to report to authorities outside the child care center should be clarified in state law. Licensing individuals as well as programs would strengthen reporting by caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".